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Comments on "Momentum fractional LMS for power signal parameter estimation"

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arxiv 1805.07640 v1 pith:TCRSSQD2 submitted 2018-05-19 math.OC cs.SYeess.SYstat.ML

classification math.OCcs.SYeess.SYstat.ML
keywords fractionalmomentumanalysisestimationleastmeanmethodparameter
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The purpose of this paper is to indicate that the recently proposed Momentum fractional least mean squares (mFLMS) algorithm has some serious flaws in its design and analysis. Our apprehensions are based on the evidence we found in the derivation and analysis in the paper titled: \textquotedblleft \textit{Momentum fractional LMS for power signal parameter estimation}\textquotedblright. In addition to the theoretical bases our claims are also verified through extensive simulation results. The experiments clearly show that the new method does not have any advantage over the classical least mean square (LMS) method.

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Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Quantum Calculus-based Volterra LMS for Nonlinear Channel Estimation

    math.OC 2019-08 reject novelty 3.0 of 10

    A q-calculus variant of Volterra LMS is presented, but for equal q it is exactly Volterra LMS with a scaled step size, so the claimed improvement is largely a step-size effect.

  2. Chaotic Time Series Prediction using Spatio-Temporal RBF Neural Networks

    stat.ML 2019-08 reject novelty 2.0 of 10

    The proposed spatio-temporal RBF network reduces, by its own equations, to a standard RBF with reindexed hidden units, making the reported accuracy gain an artifact of hyperparameter choices rather than a new architecture.

  3. Spatio-Temporal RBF Neural Networks

    stat.ML 2019-08 reject novelty 2.0 of 10

    A spatio-temporal RBF network, mathematically equivalent to a standard RBF with more hidden units, is reported to identify a nonlinear system with lower MSE, but the comparison uses unequal hyperparameters.

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